DOCUMENT RESUME

ED 472 612 EA 032 170

AUTHOR Belfield, Clive R.

TITLE Modeling School Choice: A Comparison of Public, Private-Independent, Private-Religious, and Home-Schooled Students.Occasional Paper.

INSTITUTION Columbia Univ., New York, NY. National Center for the Studyof Privatization in Education.

REPORT NO OP-49PUB DATE 2002-06-00NOTE 20p.

AVAILABLE FROM Teachers College, Columbia University, Box 181, 230 ThompsonHall, 525 West 120th Street, New York, NY 10027-6696. Tel:212-678-3259; Fax: 212-678-3474; e-mail: ncspe@columbia.edu;Web site: http://www.ncspe.org.

PUB TYPE Reports Research (143)

EDRS PRICE EDRS Price MF01/PC01 Plus Postage.DESCRIPTORS Comparative Analysis; Economic Factors; Elementary Secondary

Education; *Home Schooling; Nontraditional Education;*Private Education; *Private Schools; Public Education;*Public Schools; School Choice

ABSTRACT

U.S. students now have four choices of schooling: publicschooling, private-religious schooling, private-independent schooling, andhome-schooling. Of these options, home-schooling is the most novel. Sincelegalization across the states in the last few decades, it has grown inimportance and legitimacy as an alternative choice. Thus, it is now possibleto investigate the motivation for home-schooling, relative to the otherschooling options. Two recent large-scale datasets were used in this paper toassess the school enrollment decision: the first is the National HouseholdExpenditure Survey (1999), and the second is microdata on test-takers of thestudent achievement test in 2001. Results show that, generally, families withhome-schoolers have characteristics similar to those with children at othertypes of school, but mothers' characteristics--specifically, their employmentstatus--have a strong influence on the decision to home-school. Plausibly,religious belief has an important influence on the schooling decision, notonly for Catholic students, but also for those of other faiths. An appendixcontains a table of frequencies for independent variables. (Contains 23references and 3 tables.) (Author/RT)

Reproductions supplied by EDRS are the best that can be madefrom the original document.

4.

N

N1

Occasional Paper No 49

National Center for the Study of Privatization in Education

Teachers College, Columbia University

Modeling School Choice:

A Comparison of Public, PrivateIndependent, PrivateReligious

and Home-Schooled Students

June 2002

Clive R. Belfield

cb2001@columbia.edu

National Center for the Study of Privatization in Education

Teachers College, Columbia University

525 W120th Street

New York NY 10027-6696

www.ncspe.org

Abstract US students now have four choices of schooling: public schooling,privatereligious schooling, privateindependent schooling, and home-schooling. Ofthese, home-schooling is the most novel: since legalization across the states in the lastfew decades, it has grown in importance and legitimacy as an alternative choice. Thus, itis now possible to investigate the motivation for home-schooling, relative to the otherschooling options. Here, we use two recent large-scale datasets to assess the schoolenrollment decision: the first is the National Household Expenditure Survey (1999), andthe second is micro-data on SAT test-takers in 2001. We find that, generally, familieswith home-schoolers have similar characteristics to those with children at other types ofschool, but mother's characteristics specifically, her employment status have a stronginfluence on the decision to home-school. Plausibly, religious belief has an importantinfluence on the schooling decision, not only for Catholic students, but also those of otherfaiths.

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The Occasional Paper Series of the National Center for the Study of Privatization in

Education (NCSPE) is designed to promote dialogue about the many facets of

privatization in education. The subject matter of the papers is diverse, including research

reviews and original research on vouchers, charter schools, home schooling, and

educational management organizations. The papers are grounded in a range of

disciplinary and methodological approaches. The views presented in these papers are

those of the authors and do not necessarily reflect the official views of the NCSPE.

If you are interested in submitting a paper, or wish to learn more about the NCSPE,

please contact:

NCSPE, Box 181,

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email: ncspe@columbia.edu

Z.

1. Introduction

A sense of disaffection with public schooling both its effectiveness and efficiency has

been emphatically catalogued by academic economists (Hanushek, 1998; Hoxby, 2000;

Friedman, 1993). The general population is somewhat more ambivalent (Moe, 2001),

and indeed the proportions of students in private schools have remained stable over recent

decades (Kenny and Schmidt, 1994). However, private schooling either religious or

sectarian is not the only outlet for those dissatisfied with public schooling: home-

schooling is now a viable option.

The recent growth and development of home-schooling has been described in detail

by numerous authors (Lines, 2000; Weiner and Weiner, 1999; Hammons, 2001;

Somerville, 2000; Stevens, 2001; Bauman, 2002). These authors emphasize the legal and

civic aspects of home-schooling, but there has been little quantitative assessment or

economic treatment. This is surprising: home-schooling represents an extreme form of

education privatization, affecting the expenditure patterns, time allocation, and labor

force participation of the families involved. Furthermore, home-schooling extends the

school choice decision to four alternatives.

Here, we report the determinants of school choice decisions by US families,

contrasting each schooling option. Such school choices are easily expressed using

economic calculus. For example, home-schooling may be more effective than public

schools, and possibly less costly (if there are either high transport costs or additional

expenditures mandated by schools, e.g. uniforms, learning materials). Similarly, home-

schooling may be more effective than private schools (if these are 'elitist', and appear

hostile to outsiders), and possibly less costly (with no direct tuition fees). More

generally, home-schooling may meet the needs of families with particular educational

preferences that are not catered for by available institutions (typically for morality-based

schooling, James, 1987). In this case the appropriate comparison is between home-

schooling and religious schools.

Although precise numbers are hard to obtain, NCES (2001) estimates based on

weighted interpretation of the NHES99 indicate approximately 850,000 home-schoolers

aged from 5 to 17 (1.7% of all US students). And, the number of home-schooled children

in the US is growing (Lines, 2000): using the CPS, NHES96, and NHES99, Bauman

(2002) charts the number of home-schoolers at: 356,000 in 1994, 636,000 in 1996, and

791,000 by 1999. This figure is still small compared to the 5.1 million students in private

schools, but home-schooling has only been legal since the 1970s. Moreover, home-

schooling might be a possibility for all families during at least some part of childrearing,

with potentially important ramifications. For parents, allocations of time within the

household may be changed and labor market supply reduced; consumption of educational

materials will be affected, as will consumption of public goods and housing (via

attenuated Tiebout effects). For children, academic achievement may be affected, insofar

as parents differ as adequate substitutes for trained teachers. Also likely to be affected

are children's welfare; their social skills; and their labor market participation (if home-

schoolers are screened differently by employers). The motivation to home-school

therefore merits investigation.

Our inquiry is structured as follows. In Section 2 we model the school choice

decision across school types. In Section 3 we describe the datasets available to us. In

Section 4 we report the empirical evidence on the determinants of the school choice

decision. Section 5 concludes, referring back to the relevance of home-schooling within

the current system of US schooling.

3

2. The Economics of School Choice

Prior research on school choice in the US has mainly focused on binary options: students

decide to exit public schools for the single alternative, typically held to be Catholic

schooling (but see Figlio and Stone, 1999). However, this stylization elides religious and

non-religious schooling, even though these are unlikely to be close substitutes. It is also

out-of-date, given: changes in the teaching staff and student composition in religious

schools (on the evolution of Catholic schooling, see Sander, 2001; Grogger and Neal,

2000); the growth of other types of private school; and greater choice in the public sector

(e.g. charter schooling). Instead, the school enrollment decision is best articulated as a

four-way choice: public schooling, privatereligious schooling, privateindependent

schooling, and home-schooling.

A straightforward way to infer school choice motivation is to compare tabulations

of characteristics by school type.' For home-schooling, aggregate comparisons show

families that are: more likely to be white and non-Hispanic; have income levels

comparable to the national average (but with a more leptokurtic distribution); and have

parents who were more highly educated than the average for the US. It is not necessarily

the case that families who decide to home-school possess highly idiosyncratic attributes

(see Bauman, 2002; NHES, 2001). However, we are interested in the more general

question as to what motivates the decision to choose a particular school type.

In deciding between the schooling options, households can be assumed to maximize

utility. Neal (1997) specifies a utility function for household i where:

(1) U, = U(Y,, EC,, M,)

In (1), Y denotes the educational outcomes from schooling; EC denotes the unobserved

consumption goods from schooling (e.g. religiosity, dutifulness to parents); and M

denotes a composite commodity with a price normalized to one.' We generalize Neal's

Another method for inference is to look at what schools are chosen by families whose choice set isexpanded, e.g. through voucher programs. This literature has been summarized by Teske and Schneider(2000).2 We derive the idea of dutifulness via home-schooling from Adam Smith: "Do you wish to educate yourchildren to be dutiful [?]... educate them in your own house. From their parent's house they may, with

4

(1997) choice model to include the j=4 different types of schooling where p is public

schooling, d is privateindependent schooling, r is privatereligious schooling, and h is

home-schooling. Educational outcomes are therefore determined across each of the

choices as:

(2) Yip = X,a.p +

(3) Yid = Xiad + Ad + + 1i

(4) Yir = Xiar + Ar + ar + ii

(5) Yin = Xiah + An + an ±

In (2)(5), X denotes a vector of input and control variables. The Aj parameters identify

the match between household i and the selected school type; it is assumed that

E(4i1X,)=0. The di parameters represent the mean outcome effect for school type j relative

to public schooling. The i term is a household effect (error term) and again by

assumption E(1,1X,)=0. Such modeling is necessary to estimate the treatment effects

across school types, as well as exogenous instruments that may serve to identify the

school choice match (see Evans and Schwab, 1995). However, our inquiry is restricted,

first, to specifying the variables to be included in X, and then, second, to giving some

indication of the match between household types and school types (Aj). Such inquiry

may therefore guide the search for appropriate instruments for the school choice decision

(see the discussion in Card, 1999).

We therefore estimate a multinomial logit model, where school choice is a

function of the characteristics of the household, the child, the mother/father, and the local

community:

(6) Pr(Choice j=1..4) = f(Household, Child, Mother/father, Community)

Our aim is to identify the statistical and substantive characteristics that motivate the

choice of one school type over another. Variables capturing the child's characteristics

propriety and advantage, go out every day to attend public schools: but let their dwelling be always athome... Surely no acquirement, which can possibly be derived from what is called a public education, canmake any sort of compensation for what is almost certainly and necessarily lost by it. Domestic education isthe institution of nature; public education, the contrivance of man. It is surely unnecessary to say, which islikely to be the wisest" (2000 [1759], VI.11.13).

5

may indicate which children (in terms of ability, gender, and maturity) favor particular

school types. Parental variables may capture not only intergenerational transfers of

educational attributes, but also parental capacity for home-schooling. Of particular

interest are the household and community characteristics that influence the school choice

decision, and their relative importance across each school type. The household variables

capture the resources available within the home for educational purposes, as well as

social differences across students (see Lareau, 2000). The community variables are likely

to capture the local public resources available for schooling, and the importance of

neighborhood in schooling decisions. This estimation therefore yields several policy-

useful questions. For example, how important are household compositions (such as two-

parent families) compared to the education level of the parents? Also, do families with

special learning needs seek home-schooling as an alternative to public schools, rather

than private schools? What school types do students of religions other than Catholicism

choose? Using two similar datasets, we are able to estimate equation (6) to answer such

questions, as well as triangulate the results.

6

3 Data

Two recent datasets are available to estimate equation (6). These are the National

Household Expenditure Survey (NHES99) and micro-data from SAT test-participants in

2001 (ETS01).

NHES99 is a random-digit dialing telephone survey, with a nationally

representative sample of all civilian, non-institutionalized US persons. Screening

interviews were administered to 57,278 households (74% response rate), and then

parental interviews were conducted, where children were found to be in the household

(88% response rate post-screening). The relevant sample of parent respondents is 17,640.

To compensate for bias (arising from lack of telephone, non-response, or ethnicity),

weights are applied to the data. Whereas public and private school distinctions are

relatively straightforward, the identification of home-schooling is less clear. Here, home-

schooling is identified using the NCES (2001) definition, which is derived from

questions: 'Is child being schooled at home?'; 'Is child getting all of his/her instruction at

home?' and 'How many hours each week does child usually go to school for

instruction?'; and 'What are the main reasons you decided to school child at home?' So,

home-schooling is identified where the child is being schooled at home; where any public

schooling did not exceed 25 hours per week; and where the child is not being schooled at

home for temporary reasons of health. This definition yields 270 (1.5%) students who

are home-schooled (unweighted number). The rest of the sample is: 1,530 (8.7%)

students who attend religious school; 560 (3.2%) who attend a privateindependent

school; and 15,280 (86.6%) who attend public schools.

ETS01 is the population of individuals who took the SAT college-entry

examination in 2001. Before taking the SAT, each individual is required to complete a

background questionnaire which requests information about the household, the

individual, and the family. This information is similar to, but not exactly the same as, the

information collected in NHES99. One advantage of the ETS01 data, for instance, is that

individuals report their religion. For the characteristics of the community, county-level

data are merged into the core dataset through the individual's school location. The

county-level variable is the proportion of children aged 5 to 17 who are defined as 'poor'

7

in the Census, taken from the US Census Small Area Income and Poverty Estimates

1998, State and County 1998 (www.census.gov/hhes/saipe). Importantly, each test-

participant declares their type of schooling; and in 2001 the questionnaire included the

option 'home-schooling'.3 Based on the self-reported school types, the sample includes:

4,653 (0.01%) students who are home-schooled; 109,135 (11.3%) students who attend

religious school; 32,469 (3.4%) who attend a privateindependent school; and 822,967

(84.9%) who attend public schools.

Both datasets are recent, large-scale, and include an array of similar variables; they

are also sufficiently up-to-date to include a home-schooling indicator (although we

recognize that home-schoolers may be relatively disinclined to complete government

surveys). The important difference is that the ETS01 data refer essentially to only one

cohort of students, aged between 14 and 18 in 2001, who are attempting to gain entry to

college. Given the relative novelty of home-schooling, those who appear in ETS01 are

the 'first-movers' into home-schooling. Moreover, school choice and desire to gain entry

to college may be endogenously determined. Notwithstanding, the fact that the ETS01

test-participants are all of similar ability, ages and motivations, may serve as a control for

unobservable characteristics motivating the school choice decision. Thus, the school

choice decision can be interpreted more specifically using the ETS01 data: given a

student who wishes to go to college, what factors motivate the choice of school type?

3 The option to declare as a home-schooler was also available to test-participants in the year 2000.However, based on personal communications with ETS staff, we were persuaded that the home-schoolingindicator for 2000 may not be reliable.

8

10

4. Estimation of School Choice

The multinomial logit estimates for equation (6) are given in Tables 1 and 2, using

NHES99 and ETS01. The reported coefficients are marginal effects, i.e. differentiation

of the dependent variable with respect to the independent variable (or transformation of a

dummy variable from zero to one). Frequencies for each of the independent variables are

given in Appendix Table Al.

The results for both the NHES99 and ETS01 are plausible and suggestive (and these

results correspond broadly with those of Figlio and Stone, 1999). For household

characteristics, common to both equations is a measure of wealth, either the log of family

income, or dummy variables for home-ownership, a high-income family, or the

expectation of financial aid at college. The results across the two surveys are consistent:

family fmancial resources are strongly positively correlated with private schooling as

opposed to public schooling, and home schooling is adopted inversely with family

resource levels. Interestingly, these financing variables show the same magnitude of

effect for both independent and religious private schooling.

The NHES99 includes further details about the household: larger numbers of

adults in the household are negatively associated with religious schooling, being

associated with a shift toward public schooling. However, more children in the

household are associated essentially with a switch between privateindependent and

home-schooling.

Student characteristics play a strong role in influencing the school choice

decision. However, the results are discrepant in some cases. So, the NHES99 shows

male children are less likely to attend private-religious school, whereas the ETS01

estimation indicates the opposite. For ethnicity, the results are more in accord: both

African American and Latino students are more likely to attend public school, and least

likely to attend private religious school; Asian students are spread more evenly across the

options, although they too are least likely to attend private religious school. Similarly,

private-religious schools are least likely to enroll US (immigrant) citizens. The age

variable in the NHES99 survey shows that private schools particularly religious ones

primarily serve younger students; home-schooling appears to be prevalent across all ages.

9

Of special interest in the debate about choice is the disability of the child: opponents of

choice have argued that private schools will subtly dissuade children with additional

learning needs from enrollment (see the discussion in Howell and Peterson, 2002). For

the private independent schools, there is no evidence of such dissuasion: students with

disabilities or special learning needs are more likely to be in these schools. Again, home-

schooling appears as a neutral option, whereas (according to NHES99, but not ETS01)

private-religious schools do enroll fewer disabled students. Finally, the ETS01 data

includes information on religious status. This variable has a strong effect: students who

profess any religion are more likely to be in private-religious schooling, but less likely to

be in private-independent schooling. The results for home-schooling are mixed: those

following the Catholic faith are less likely to be home-schooled, but other religions do

dispose the family toward this choice. Overall, however, the marginal coefficients for

religion as a determinant of school choice is between 2 and 10 times that of any other

factors.

Maternal characteristics are identified by education levels, and by whether the

mother is employed or not (NHES99 only). Relative to mothers who had not obtained a

high school equivalency, the effect of more education is to switch enrolment away from

public schools toward the other three options. The NHES99 results show higher maternal

education is a strong influence on home-schooling, and this finding is to some extent

supported by the ETS01 estimation. Again, however, these educational influences are

strongest in causing a switch toward private religious schooling. Similarly, if the mother

is employed, the child is much more likely to be in public school, with the other three

options being equally affected positively.

Finally, Tables 1 and 2 show the effects of community characteristics. Higher

rates of poverty are more likely to encourage private schooling (presumably amongst

those who are not below the poverty line themselves). (Median household income at the

county level an alternative community income measure for the ETS01 survey is

highly correlated with the poverty rate, and so is omitted from the analysis). Plausibly,

home schooling is less common in the North East, and urban areas; these are areas where

private schooling options are more common.

Before concluding, it is worthwhile noting some of the possible caveats to these

findings. The first is the difficulty of measuring home-schooling, and finding out

precisely what type of educational choice it represents. Some parents may temporarily

home-school, e.g. for a single academic year; others may home-school part-time, e.g.

enrolling only half-days at public school. For the NHES99 data, there is a reasonably

agreed definition for home-schooling, but the ETS01 data includes self-reports of school

type. The second caveat is that the sample of home-schoolers is too small; certainly,

more efficient estimates would be obtained with larger samples (both absolute and

relative to the high proportions enrolled in the other school types). Nevertheless, the

ETS01 data includes over 5,000 home-schoolers in its sample. Finally, a third possible

caveat is that the multinomial logit estimation may be improperly conceived. However,

testing for the 'irrelevance of independent assumptions' by pooling religious and

independent students does not materially influence the coefficients (on the other two

school types).

11

5 Conclusion

Here, a simple model of choice is used to explore the determinants of school choice,

represented through the four options now available to parents. The aim has been modest:

to see what factors are important when school choice is being decided on. In this respect

the results are not surprising.

However, the evidence has a more purposeful application. First, it shows how

different factors motivate different choices. So, families are more disposed toward home-

schooling and away from private-independent schooling when there are more children in

the house; but they are more disposed away from home schooling and toward public

schooling when the mother works. Income variables and community poverty rates tend

to sway parents toward private schooling, but not toward home-schooling. Second, the

evidence can elucidate which type of schooling is most divergent from the public school

norm, i.e. which school type has the strongest 'independent' characteristics. Based on

Table 1 and 2, it appears that the families who use private-religious schools have especial

characteristics, strongly attracting them to this choice. Therefore, it is the religious

schools and not the home-schoolers that appear the 'most different' from public

schools, at least along the vector of characteristics for which there are data. Finally, this

inquiry may be useful for directing the search for instrumental variables for school

choice. Religious belief appears as the most substantively powerful influence in choosing

private schooling. In magnitude, the influence of religious persuasion far outweighs that

of family resources or maternal education levels. Notwithstanding the criticisms leveled

at such a variable (Altonji et al., 1999), it may still be appropriate to model the supply of

religious schooling within treatment equations such as (2)-(5) above. For home-

schooling decisions; instrumental variables might be derived from the opportunities for,

or the need for, mothers to enter the fabor market.

Acknowledgements

The authors appreciate the effort of Drew Gitomer and the Educational Testing Service formaking the SAT data available.

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Policy Analysis Archives, 10 (26).Card, D. 1999. The causal effect of education on earnings. In Ashenfelter 0 and D Card. (Eds).

Handbook of Labor Economics Volume 3C. North-Holland: New York.Evans, WN and RM Schwab. 1995. Finishing high school and starting college: do Catholic

schools make a difference? Quarterly Journal of Economics, CX, 941-974.Figlio, DN and JA Stone. 1999. Are private schools really better? Research in Labor Economics,

18,115-140.Friedman, M. 1993. Public schools, make them private. Education Economics, 1, 25-45.Grogger, J and D Neal. 2000. Further evidence on the benefits of Catholic secondary schooling.

Brookings-Wharton Papers on Urban Affairs. Brookings: Washington, DC.Hammons, CW. 2001. School@home. Education Next, Winter, 48-55.Hanushek, EA. 1998. Conclusions and controversies about the effectiveness of schools. Federal

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Brookings-Wharton Papers on Urban Affairs, pp. 147-202.Moe, TM. 2001. Schools, Vouchers and the American Public. Brookings Institution: Washington.National Center for Education Statistics (NCES). 2001. Homeschooling in the United States:

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Movement. Princeton University Press: Princeton.Teske, P and M Schneider. 2000. What research can tell policymakers about school choice.

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13

15

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0(0

.002

2)**

*-0

.013

9(0

.002

2)**

*E

thni

city

": A

sian

0.01

28(0

.012

2)-0

.000

2(0

.005

2)-0

.011

3(0

.005

2)**

-0.0

013

(0.0

052)

Eth

nici

ty":

His

pani

c0.

0489

(0.0

062)

***

-0.0

038

(0.0

020)

*-0

.028

9(0

.002

0)**

*-0

.016

2(0

.002

0)**

*B

orn

outs

ide

US

0.01

95(0

.011

2)*

-0.0

031

(0.0

028)

-0.0

158

(0.0

028)

***

-0.0

006

(0.0

028)

Age

': 10

to 1

2 ye

ars

0.01

08(0

.005

8)*

0.00

04(0

.001

9)-0

.006

2(0

.001

9)**

*-0

.005

0(0

.001

9)**

*A

ge':

13 to

18

year

s0.

0315

(0.0

054)

***

0.00

15(0

.001

7)-0

.026

2(0

.001

7)**

*-0

.006

8(0

.001

7)**

*Sp

ecia

l Lea

rnin

g N

eeds

0.01

23(0

.005

8)**

-0.0

004

(0.0

015)

-0.0

145

(0.0

015)

***

0.00

27(0

.001

5)*

Mot

her's

cha

ract

eris

tics:

Edu

c.d:

Hig

h Sc

hool

-0.0

257

(0.0

137)

*0.

0241

(0.0

081)

***

0.00

46(0

.008

1)-0

.003

1(0

.008

1)E

duc.

d: S

ome

Col

lege

-0.0

554

(0.0

177)

***

0.03

70(0

.013

6)**

*0.

0169

(0.0

136)

0.00

15(0

.013

6)E

duc.

d: (

Hig

her)

Deg

ree

-0.1

131

(0.0

213)

***

0.04

73(0

.017

0)**

*0.

0420

(0.0

170)

**0.

0239

(0.0

170)

Mot

her:

Em

ploy

ed0.

0409

(0.0

064)

***

-0.0

161

(0.0

027)

***

-0.0

121

(0.0

027)

***

-0.0

128

(0.0

027)

***

14

Tab

le 1

(C

ontd

)

Com

mun

ity c

hara

cter

istic

s:Z

IP p

over

ty li

ne':

>10

%-0

.008

6 (0

.007

0)0.

0004

(0.

0017

)0.

0069

(0.

0017

)***

0.00

13 (

0.00

17)

ZIP

His

p-B

lack

(: 0

-15%

0.04

87 (

0.00

91)*

**0.

0037

(0.

0028

)-0

.032

0 (0

.002

8)**

*-0

.020

4 (0

.002

8)**

*Z

IP H

isp-

Bla

ck(:

16-

40%

0.01

26 (

0.00

77)

0.00

77 (

0.00

40)*

-0.0

150

(0.0

040)

***

-0.0

053

(0.0

040)

Reg

ions

: Nor

th E

ast

-0.0

534

(0.0

106)

***

-0.0

041

(0.0

019)

**0.

0450

(0.

0019

)***

0.01

25 (

0.00

19)*

**R

egio

ns: S

outh

-0.0

315

(0.0

075)

***

0.00

11 (

0.00

18)

0.02

67 (

0.00

18)*

**0.

0037

(0.

0018

)**

Reg

ions

: Mid

wes

t-0

.047

3 (0

.010

4)**

*-0

.003

0 (0

.001

7)*

0.05

65 (

0.00

17)*

**-0

.006

1 (0

.001

7)**

*A

reah

: Urb

an-0

.065

1 (0

.006

3)**

*-0

.002

0 (0

.001

6)0.

0536

(0.

0016

)***

0.01

35 (

0.00

16)*

**A

reah

: Sub

urba

n-0

.034

6 (0

.012

8)**

*-0

.000

6 (0

.001

9)0.

0338

(0.

0019

)***

0.00

14 (

0.00

19)

Pred

icte

d Pr

ob.

0.90

900.

0086

0.06

230.

0200

Pseu

do R

Squ

ared

0.09

92

Log

Lik

elih

ood

7338

.80

Wal

d C

hi2

(75)

1071

.57

N17

640

1.04

Not

es: P

aren

t Sam

ple,

Nat

iona

l Hou

seho

ld E

duca

tion

Surv

ey (

NH

ES,

199

9). R

obus

t sta

ndar

d er

rors

in p

aren

thes

es.

'Def

ault

adul

ts: 1

par

ent o

nly.

bDef

ault

ft,2

ethn

icity

: whi

te. '

Def

ault

age:

5-9

yea

rs. d

Def

ault

educ

atio

n le

vel:

less

than

Hig

h sc

hool

. 'D

efau

lt Z

IP p

over

ty li

ne: >

19%

.(D

efau

lt Z

IP H

isp-

Bla

ck: >

40%

.sD

efau

lt re

gion

: wes

t. hD

efau

lt ar

ea: r

ural

. ***

sign

ific

ance

at 0

.01

leve

l; **

sign

ific

ance

at 0

.05

leve

l; *s

igni

fica

nce

at 0

.10

leve

l.

Tab

le 2

Det

erm

inan

ts o

f th

e D

ecis

ion

to H

ome-

Scho

ol o

r to

Enr

oll a

t Rel

igio

us o

r N

on-R

elig

ious

Pri

vate

Sch

ool v

ersu

s Pu

blic

Sch

oolin

g: E

TS0

1(M

ultin

omia

l Log

it E

stim

atio

n M

argi

nal E

ffec

ts)

Publ

icSc

hool

Hom

eSc

hool

Priv

ate

Rel

igio

usSc

hool

Priv

ate

Inde

pend

ent

Scho

ol

Mar

gina

l(S

E)

Coe

ff.

Mar

gina

l(S

E)

Coe

ff.

Mar

gina

lC

oeff

.(S

E)

Mar

gina

lC

oeff

.(S

E)

Hou

seho

ld c

hara

cter

istic

s:Fa

mily

inco

me

> $

100,

000

-0.0

347

(0.0

026)

***

-0.0

020

(0.0

003)

***

0.02

52(0

.002

3)**

*0.

0116

(0.0

012)

***

Fina

ncia

l aid

s0.

0349

(0.0

024)

***

0.00

01(0

.000

3)-0

.013

4(0

.002

1)**

*-0

.021

5(0

.001

3)**

*

Stud

ent's

cha

ract

eris

tics:

Mal

e-0

.013

0(0

.002

0)**

*0.

0005

(0.0

003)

*0.

0105

(0.0

018)

***

0.00

20(0

.000

9)**

Eth

nici

tyb:

Afr

ican

Am

er.

0.02

05(0

.003

4)**

*-0

.002

7(0

.000

3)**

*-0

.009

7(0

.003

2)**

*-0

.008

1(0

.001

3)**

*E

thni

city

b: A

sian

0.00

79(0

.004

0)**

-0.0

025

(0.0

003)

***

-0.0

095

(0.0

035)

***

0.00

41(0

.001

9)**

Eth

nici

tyb:

His

pani

c0.

0194

(0.0

034)

***

-0.0

016

(0.0

004)

***

-0.0

098

(0.0

030)

***

-0.0

079

(0.0

016)

***

Bor

n ou

tsid

e U

S0.

0615

(0.0

039)

***

-0.0

020

(0.0

006)

***

-0.0

503

(0.0

034)

***

-0.0

092

(0.0

020)

***

Dis

abili

ty-0

.015

8(0

.003

7)**

*-0

.000

7(0

.000

4)*

0.00

09(0

.003

2)0.

0156

(0.0

020)

***

Rel

igio

n': C

atho

lic-0

.187

0(0

.004

7)**

*-0

.001

9(0

.000

4)**

*0.

2017

(0.0

047)

***

-0.0

128

(0.0

009)

***

Rel

igio

n': O

ther

fai

ths

-0.0

246

(0.0

028)

***

0.00

26(0

.000

4)**

*0.

0261

(0.0

027)

***

-0.0

041

(0.0

010)

***

Mot

her's

cha

ract

eris

tics:

Edu

c.d:

Hig

h Sc

hool

-0.0

607

(0.0

079)

***

0.00

75(0

.004

4)*

0.05

05(0

.006

5)**

*0.

0027

(0.0

033)

Edu

c.d:

Som

e C

olle

ge-0

.081

1(0

.007

7)**

*0.

0100

(0.0

045)

**0.

0558

(0.0

060)

***

0.01

53(0

.003

7)**

*

Edu

c.d:

(H

ighe

r) D

egre

e-0

.124

5(0

.007

4)**

*0.

0082

(0.0

035)

**0.

0810

(0.0

060)

***

0.03

52(0

.004

3)**

*

16

Tab

le 2

(C

ontd

)

Com

mun

ity c

hara

cter

istic

s:C

ount

y po

vert

y ra

te`

-0.0

031

(0.0

001)

***

-0.0

000

(0.0

000)

0.00

25 (

0.00

01)*

**0.

0006

(0.

0001

)***

Reg

ionf

: Nor

th E

ast

-0.0

135

(0.0

029)

***

-0.0

013

(0.0

004)

***

0.00

33 (

0.00

25)

0.01

14 (

0.00

15)*

**

Reg

ionf

: Sou

th0.

0123

(0.

0028

)***

0.00

07 (

0.00

04)*

-0.0

236

(0.0

024)

***

0.01

06 (

0.00

14)*

**R

egio

nf: M

idw

est

-0.0

403

(0.0

043)

***

0.00

11 (

0.00

06)*

0.03

52 (

0.00

40)*

**0.

0040

(0.

0020

)**

Pred

icte

d Pr

ob.

0.88

560.

0028

0.08

870.

0229

Pseu

do R

Squ

ared

0.09

66

Log

Lik

elih

ood

4506

2.90

Wal

d C

hi2

(51)

9636

.54

N96

9223

Not

es: E

duca

tion

Tes

ting

Serv

ice

(ET

S, 2

001)

. Rob

ust s

tand

ard

erro

rs in

par

enth

eses

.'D

umm

y va

riab

le in

dica

ting

stud

ent a

ntic

ipat

es o

btai

ning

fin

anci

al a

idfo

r hi

gher

edu

catio

n. b

Def

ault

ethn

icity

: whi

te. '

Def

ault:

no

relig

ion

(or

pref

erre

d no

t to

answ

er).

dD

efau

lt ed

ucat

ion

leve

l: le

ss th

an H

igh

scho

ol. '

Cen

sus

data

.(D

efau

lt re

gion

: wes

t. **

*sig

nifi

canc

e at

0.0

1 le

vel;

**si

gnif

ican

ce a

t 0.0

5 le

vel;

*sig

nifi

canc

e at

0.1

0 le

vel.

17

Appendix Table AlFrequencies for Independent Variables

NHES99

Mean (SD)

Household characteristics:Owns home 0.69Family income 49010.00 (31150.00)Adults: 2 (both parents) 0.53Adults: 2 (one parent) 0.08Adults: 3 or more 0.20Siblings for child 1.28 (0.34)Family income > $100,000Financial aid

Student's characteristics:Male 0.51

Ethnicity: African Amer. 0.16Ethnicity: Asian 0.03Ethnicity: Hispanic 0.18Born outside US 0.05Age: 10 to 12 years 0.23

Age: 13 to 18 years 0.45Special learning needs 0.23

DisabilityReligion: CatholicReligion: Other faiths

Mother's characteristics:Educ.: High school 0.39Educ.: Some college 0.26Educ.: (Higher) degree 0.25Mother employed 0.67

Community characteristics:ZIP poverty line: >10% 0.32ZIP Hisp-Black: 0-15% 0.51

ZIP Hisp-Black: 16-40% 0.26County poverty lineCounty median incomeRegion: North East 0.17Region: South 0.39Region: Midwest 0.20Area: Urban 0.66Area: Suburban 0.13

N 17640

18

40

ETS01

Mean (SD)

0.230.74

0.450.110.080.090.04

0.080.240.47

0.080.320.39

17.85 (7.36)43083.16 (10264.86)

0.320.350.110.350.52

969223

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Title: Modeling School Choice: A Comparison of Public, Private-Independent,Private-Religious and Home-Schooled StudentsOccasional Paper No. 49 National Center for the Study of Privatization in Education

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